Threat-Map-Based Trajectory Planning for RLVs in Space Debris Environments

Le résumé fourni par la source

Compared with traditional aircraft, Reusable Launch Vehicles (RLVs) exhibit more complex mission profiles, requiring rapid maneuvering across wide speed and time domains. During the orbital phase (at altitudes of 100-400 km), RLVs face an increasingly severe space debris environment. In recent years, the continuous growth of launch missions has led to a sharp increase in debris population in near-Earth orbit, urgently necessitating the development of safe trajectory planning methods for RLVs operating in high-density debris environments. This paper proposes a trajectory planning method based on a collision probability threat map to enhance the safe navigation capability of RLVs in debris-dense regions. First, an integrated threat assessment mechanism is established: building upon collision probability, it incorporates multiple threat factors, including obstacle physical dimensions and relative velocity, to enhance threat evaluation accuracy through multi-factor fusion. Based on this mechanism, a threat map is constructed where each grid cell is labeled with a comprehensive threat value. This approach transforms the complex three-dimensional obstacle avoidance problem in debris environments into a path optimization problem based on the threat map. At the trajectory planning level, an improved bidirectional Rapidly-exploring Random Tree (RRT) algorithm is adopted, which converts threat map information into the algorithm’s cost function and sampling strategy to achieve safe and efficient path search. Specifically, a composite cost function based on path length and comprehensive threat value is designed to simultaneously optimize trajectory economy and safety. A threat-aware intelligent sampling mechanism is developed to guide the search tree toward low-threat regions. By leveraging the guidance of the threat map, this method effectively overcomes the randomness defects of traditional RRT* algorithms while ensuring asymptotic optimality and significantly improving convergence efficiency. Finally, simulation results demonstrate that the proposed threat map-based trajectory planning algorithm can ensure safe passage through dense space debris regions.

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